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arXiv · 2506.08298

H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs

Trung-Kien Nguyen·Heng Ping·Shixuan Li·Peiyu Zhang·Nikos Kanakaris·Nicholas Kotov·Paul Bogdan
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Abstract

The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, known as the Graph Foundation Model (GFM). Existing research has leveraged text-attributed graphs (TAGs) to tackle the heterogeneity in node features among graphs. However, they primarily focus on homogeneous TAGs (HoTAGs), leaving heterogeneous TAGs (HeTAGs), where multiple types of nodes/edges reside, underexplored. To enhance the capabilities and applications of GFM, we introduce H$^2$GFM, a novel framework designed to generalize across both HoTAGs and HeTAGs. Our model projects diverse meta-relations among graphs under a unified textual space, and employs a context encoding to capture spatial and higher-order semantic relationships. To achieve robust node representations, we propose a novel context-adaptive graph transformer (CGT), effectively capturing information from both context neighbors and their relationships. Furthermore, we employ a mixture of CGT experts to capture the heterogeneity in structural patterns among graph types. Comprehensive experiments on a wide range of HoTAGs and HeTAGs as well as learning scenarios demonstrate the effectiveness of our model.

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Trung-Kien Nguyen, Heng Ping, Shixuan Li, Peiyu Zhang, Nikos Kanakaris, Nicholas Kotov, Paul Bogdan. 2025-06-10. H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs. https://arxiv.org/abs/2506.08298

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